






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


__A					=	logging.get_logger(__name__)



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							Union[str, Any]				= ['pixel_values']



    def __init__(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Dict[str, int] = None ,     __SCREAMING_SNAKE_CASE      :     PILImageResampling = PILImageResampling.BICUBIC ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Union[int, float] = 1 / 255 ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     **__SCREAMING_SNAKE_CASE      :     Tuple ,     )      ->							None:
           super().__init__(**__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=size if size is not None else {"""height""": 384, """width""": 384}
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE ,     default_to_square=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=do_resize
           __UpperCAmelCase     				=size
           __UpperCAmelCase     				=resample
           __UpperCAmelCase     				=do_rescale
           __UpperCAmelCase     				=rescale_factor
           __UpperCAmelCase     				=do_normalize
           __UpperCAmelCase     				=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           __UpperCAmelCase     				=image_std if image_std is not None else OPENAI_CLIP_STD
           __UpperCAmelCase     				=do_convert_rgb



    def _a							(				self      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     np.ndarray ,     __SCREAMING_SNAKE_CASE      :     Dict[str, int] ,     __SCREAMING_SNAKE_CASE      :     PILImageResampling = PILImageResampling.BICUBIC ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None ,     **__SCREAMING_SNAKE_CASE      :     Dict ,     )      ->							np.ndarray:
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE ,     default_to_square=__SCREAMING_SNAKE_CASE      )
           if "height" not in size or "width" not in size:
                  raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''      )
           __UpperCAmelCase     				=(size["""height"""], size["""width"""])
           return resize(__SCREAMING_SNAKE_CASE ,     size=__SCREAMING_SNAKE_CASE ,     resample=__SCREAMING_SNAKE_CASE ,     data_format=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     np.ndarray ,     __SCREAMING_SNAKE_CASE      :     Union[int, float] ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None ,     **__SCREAMING_SNAKE_CASE      :     Any ,     )      ->							Dict:
           return rescale(__SCREAMING_SNAKE_CASE ,     scale=__SCREAMING_SNAKE_CASE ,     data_format=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     np.ndarray ,     __SCREAMING_SNAKE_CASE      :     Union[float, List[float]] ,     __SCREAMING_SNAKE_CASE      :     Union[float, List[float]] ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None ,     **__SCREAMING_SNAKE_CASE      :     Any ,     )      ->							np.ndarray:
           return normalize(__SCREAMING_SNAKE_CASE ,     mean=__SCREAMING_SNAKE_CASE ,     std=__SCREAMING_SNAKE_CASE ,     data_format=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     ImageInput ,     __SCREAMING_SNAKE_CASE      :     Optional[bool] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Dict[str, int]] = None ,     __SCREAMING_SNAKE_CASE      :     PILImageResampling = None ,     __SCREAMING_SNAKE_CASE      :     Optional[bool] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[float] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[bool] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[str, TensorType]] = None ,     __SCREAMING_SNAKE_CASE      :     bool = None ,     __SCREAMING_SNAKE_CASE      :     ChannelDimension = ChannelDimension.FIRST ,     **__SCREAMING_SNAKE_CASE      :     List[str] ,     )      ->							PIL.Image.Image:
           __UpperCAmelCase     				=do_resize if do_resize is not None else self.do_resize
           __UpperCAmelCase     				=resample if resample is not None else self.resample
           __UpperCAmelCase     				=do_rescale if do_rescale is not None else self.do_rescale
           __UpperCAmelCase     				=rescale_factor if rescale_factor is not None else self.rescale_factor
           __UpperCAmelCase     				=do_normalize if do_normalize is not None else self.do_normalize
           __UpperCAmelCase     				=image_mean if image_mean is not None else self.image_mean
           __UpperCAmelCase     				=image_std if image_std is not None else self.image_std
           __UpperCAmelCase     				=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           __UpperCAmelCase     				=size if size is not None else self.size
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE ,     default_to_square=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=make_list_of_images(__SCREAMING_SNAKE_CASE      )

           if not valid_images(__SCREAMING_SNAKE_CASE      ):
                  raise ValueError(
                      """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                      """torch.Tensor, tf.Tensor or jax.ndarray."""      )

           if do_resize and size is None or resample is None:
                  raise ValueError("""Size and resample must be specified if do_resize is True."""      )

           if do_rescale and rescale_factor is None:
                  raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

           if do_normalize and (image_mean is None or image_std is None):
                  raise ValueError("""Image mean and std must be specified if do_normalize is True."""      )

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                  __UpperCAmelCase     				=[convert_to_rgb(__SCREAMING_SNAKE_CASE      ) for image in images]

           # All transformations expect numpy arrays.
           __UpperCAmelCase     				=[to_numpy_array(__SCREAMING_SNAKE_CASE      ) for image in images]

           if do_resize:
                  __UpperCAmelCase     				=[self.resize(image=__SCREAMING_SNAKE_CASE ,     size=__SCREAMING_SNAKE_CASE ,     resample=__SCREAMING_SNAKE_CASE      ) for image in images]

           if do_rescale:
                  __UpperCAmelCase     				=[self.rescale(image=__SCREAMING_SNAKE_CASE ,     scale=__SCREAMING_SNAKE_CASE      ) for image in images]

           if do_normalize:
                  __UpperCAmelCase     				=[self.normalize(image=__SCREAMING_SNAKE_CASE ,     mean=__SCREAMING_SNAKE_CASE ,     std=__SCREAMING_SNAKE_CASE      ) for image in images]

           __UpperCAmelCase     				=[to_channel_dimension_format(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) for image in images]

           __UpperCAmelCase     				=BatchFeature(data={"""pixel_values""": images} ,     tensor_type=__SCREAMING_SNAKE_CASE      )

           return encoded_outputs












'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__lowerCAmelCase        =							TypeVar("T")




class   __SCREAMING_SNAKE_CASE       (Generic[T]       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			data
         a_						     =			None







  def __str__(			self	):






         """simple docstring"""


         return f'{self.data}'





class   __SCREAMING_SNAKE_CASE       (Generic[T]       ):


  """simple docstring"""


  def __init__(			self	):






         """simple docstring"""


         a_						     =			None







  def __iter__(			self	):






         """simple docstring"""


         a_						     =			self.top
         while node:
                yield node.data
                a_						     =			node.next







  def __str__(			self	):






         """simple docstring"""


         return "->".join([str(UpperCamelCase__	) for item in self]	)







  def __len__(			self	):






         """simple docstring"""


         return len(tuple(iter(self	)	)	)







  def    _a       (			self	):






         """simple docstring"""


         return self.top is None







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			Node(UpperCamelCase__	)
         if not self.is_empty():
                a_						     =			self.top
         a_						     =			node







  def    _a       (			self	):






         """simple docstring"""


         if self.is_empty():
                raise IndexError('pop from empty stack'	)
         assert isinstance(self.top   ,	UpperCamelCase__	)
         a_						     =			self.top
         a_						     =			self.top.next
         return pop_node.data







  def    _a       (			self	):






         """simple docstring"""


         if self.is_empty():
                raise IndexError('peek from empty stack'	)

         assert self.top is not None
         return self.top.data







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			None


if __name__ == "__main__":
  from doctest import testmod

  testmod()




